1. Field of the Invention
This invention generally relates to medical imaging; and more particularly, to the analysis of microscopic images from tissue sections.
2. Description of the Related Art
As before in radiology, now with the digitization of pathology, more precisely the imaging of histology slides, new computer-assisted methods can be used that go far beyond the ability of a human evaluation and interpretation of a tissue section using an optical microscope.
The scoring and interpretation schemes today have been designed for human evaluation and interpretation, and are therefore limited in complexity and required precision. Pathologists use mostly qualitative, but also semi-quantitative and quantitative assessments of single biomarker expressions in tissue sections.
One type of a quantitative scoring scheme is based on a classification of the cells into four ranked categories: 0, 1+, 2+ and 3+. The cells are counted per cell classification category and a discrete ranked score is determined by applying thresholds to the percentages of cells for those four cell classification categories.
Although useful, this type of scoring is limited when taking into consideration computerized systems and their abilities for acquisition of tissue analysis data.
The IHC HER2 scoring scheme described in Wolff et. al. (See References) is an example of such a quantitative scoring scheme. Cells are classified into the categories: 0, 1+, 2+ and 3+ based on the combination of two cell features, membrane staining intensity and membrane completeness, according to Table 1.
TABLE 1Cell Membrane Membrane ClassificationStaining IntensityCompleteness0 NegativeNA1+Weak PositivePartial2+Medium PositiveComplete3+Strong PositiveComplete
The cells are counted per cell classification category N(c), as expressed in Eq. 1.
                                          N            ⁡                          (              c              )                                =                                                    ∑                Cells                            ⁢                              Classification                ⁡                                  (                  Cell                  )                                                      =            c                          ;                  c          ⁢                                          ⁢          •          ⁢                      {                          0              ,                              1                +                            ,                              2                +                            ,                              3                +                                      }                                              Eq        .                                  ⁢        1            
The inverse cumulative percentages ICP(c) for 3+ cells, 3+ and 2+ cells and 3+, 2+ and 1+ cells are calculated, as expressed in Eq. 2. Note that “inverse” stands here for the fact that the percentages are cumulated from high to low ranked categories, as opposed to the standard way from low to high.
                                                        ICP              ⁡                              (                c                )                                      =                                          ∑                                  i                  =                  c                                                  3                  +                                            ⁢                              P                ⁡                                  (                  i                  )                                                              ;                      c            ⁢                                                  ⁢            •            ⁢                          {                                                1                  +                                ,                                  2                  +                                ,                                  3                  +                                            }                        ⁢                                                  ⁢            with                          ⁢                                  ⁢                              P            ⁡                          (              i              )                                =                                                                      N                  ⁡                                      (                    i                    )                                                  N                            ⁢                                                          ⁢              and              ⁢                                                          ⁢              N                        =                                          ∑                                  i                  =                  0                                                  3                  +                                            ⁢                              N                ⁡                                  (                  i                  )                                                                                        Eq        .                                  ⁢        2            
Thresholds T(c) of 10%, 10% and 30% are defined corresponding to the inverse cumulative percentages ICP(c). The scores S(c) are defined as 0, 1+, 2+ and 3+ and are associated with the satisfaction of the threshold criteria ICP(c)≧T(c), as expressed in Eq. 3. Note that in the case of the IHC HER2 scoring scheme, the cell classification categories and the scores use the same ranked categories.Score=max{S(0),S(c)×[ICP(c)≧T(c)];c□{1+,2+,3+}}  Eq.3:                with S(0)=0        with S(1+)=1+ and T(1+)=10%        with S(2+)=2+ and T(2+)=10%        with S(3+)=3+ and T(3+)=30%        
FIGS. 1(A-C) illustrate an example of the IHC HER2 scoring scheme. FIG. 1A illustrates the cells (circles) in an image of a tissue section, which are color-coded according to their classification (0—blue, 1+—yellow, 2+—orange and 3+—red). FIG. 1B shows the percentages of cells for the ranked cell classification categories. FIG. 1C shows the inverse cumulative percentages for the ranked cell classification categories. The thresholds T(1+)=10%, T(2+)=10% and T(3+)=30% (red) that apply to the corresponding inverse cumulative percentages ICP(1+), ICP(2+) and ICP(3+) (black) are shown as bold lines in the different cell classification categories. In this example, the highest cell classification category, where the inverse cumulative percentage is equal to or higher than the threshold, is 2+, illustrated by the green arrow. The score, corresponding to the cell classification category 2+, is 2+.
Discrete scores, like the one provided by the IHC HER2 scoring scheme, provide a classification into clinically-relevant categories, but make it hard to identify borderline cases and to provide a more precise and accurate assessment. While discrete scoring schemes seem to be appropriate for a subjective human evaluation and interpretation, sophisticated image analysis programs that objectively detect the cells on entire tissue sections and quantify the expression of biomarkers can leverage the use of continuous scoring schemes to provide more precise and accurate assessments.
Another limitation of existing scoring schemes designed for human evaluation and interpretation is the complexity of the cell classification. The IHC HER2 scoring scheme exhibits an already rather complex cell classification schemes, as it evaluates two cell features, which are still apparently related to the expression of a single biomarker. Using sophisticated image analysis programs that allow characterizing multiple cell features at the same time enables the use of more complex cell classification schemes based on multiple cell features to provide more precise and accurate assessments.
Continuous scoring schemes can be developed by expansion of already well-known and discrete scoring schemes that are based on cell classifications. New scoring schemes can be devised that rely on complex cell classification schemes incorporating multiple cell features.
Ultimately a pathologist can use computer-assisted scoring as an aid in their evaluation and interpretation of biomarker expressions in tissue sections.